Predicting the Objective and Priority of Issue Reports in Software Repositories

TL;DR

This paper presents a novel two-stage machine learning approach, including fine-tuning a Transformer model, to automatically classify the objective and priority of GitHub issue reports, improving issue management efficiency.

Contribution

It introduces the first fine-tuned Transformer model for issue classification and evaluates its effectiveness in both project-specific and cross-project scenarios.

Findings

Achieves 82% accuracy in predicting issue objectives

Attains 75% accuracy in predicting issue priority levels

Cross-project model reaches 90% accuracy on unseen data

Abstract

Developers collaboratively discuss, implement, use, and share software entities hosted on software repositories. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team's effort. An issue report is a rich source of collaboratively curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub's top 1000 repositories do not have any labels. We aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and state-of-the-art text classifiers. To the best of our knowledge, we are the first to fine-tune a Transformer for issue classification. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with 82% and 75% accuracy, respectively. Moreover, we conducted human labeling and evaluation on unlabeled issues from six unseen GitHub projects to assess the performance of the cross-project model on new data. The model achieves 90% accuracy. We obtain 85% average Percent Agreement and 71% Randolph's free-marginal Kappa translating to substantial agreement among labelers.